首个覆盖全向360度多模态的恶劣天气深度估计数据集
The ADUULM-360 Dataset -- A Multi-Modal Dataset for Depth Estimation in Adverse Weather
- 融合相机、激光雷达与雷达的360度多模态感知数据
- 包含良好与恶劣天气下的多样化场景，支持自监督深度学习训练
- 适合自动驾驶、多传感器融合与恶劣环境感知研究者使用
深度估计是实现完整场景理解的关键任务，可将摄像头捕获的丰富语义信息投影到三维空间。尽管该领域近年来备受关注，现有深度估计数据集在场景多样性或传感器模态上仍显不足。本文提出ADUULM-360数据集，一个全新的多模态深度估计数据集。该数据集涵盖自动驾驶所有主流传感器模态：前端双目相机、六路环视相机（覆盖360度）、两台高分辨率远距激光雷达及五台远距雷达。它是首个包含良好与恶劣天气下多样化场景的深度估计数据集。我们采用先进的自监督深度估计方法，在单目、双目及全向环视等多种训练任务下开展大量实验。分析结果揭示了当前主流方法在恶劣天气下的普遍局限性，有望激发该方向未来研究。数据集、开发工具包及预训练基线模型已开源，详见https://github.com/uulm-mrm/aduulm_360_dataset。
原文摘要 · Abstract (English)
Depth estimation is an essential task toward full scene understanding since it allows the projection of rich semantic information captured by cameras into 3D space. While the field has gained much attention recently, datasets for depth estimation lack scene diversity or sensor modalities. This work presents the ADUULM-360 dataset, a novel multi-modal dataset for depth estimation. The ADUULM-360 dataset covers all established autonomous driving sensor modalities, cameras, lidars, and radars. It covers a frontal-facing stereo setup, six surround cameras covering the full 360-degree, two high-resolution long-range lidar sensors, and five long-range radar sensors. It is also the first depth estimation dataset that contains diverse scenes in good and adverse weather conditions. We conduct extensive experiments using state-of-the-art self-supervised depth estimation methods under different training tasks, such as monocular training, stereo training, and full surround training. Discussing these results, we demonstrate common limitations of state-of-the-art methods, especially in adverse weather conditions, which hopefully will inspire future research in this area. Our dataset, development kit, and trained baselines are available at https://github.com/uulm-mrm/aduulm_360_dataset.
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